Pelin Icer | Georgia State University (original) (raw)

Pelin Icer

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Papua New Guinea Institute Of Medical Research

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Papers by Pelin Icer

Springer eBooks, 2022

Genomic epidemiology is now widely used for viral outbreak investigations. Still, this methodolog... more Genomic epidemiology is now widely used for viral outbreak investigations. Still, this methodology faces many challenges. First, few methods account for intra-host viral diversity. Second, maximum parsimony principle continues to be employed, even though maximum likelihood or Bayesian models are usually more consistent. Third, many methods utilize case-specific data, such as sampling times or infection exposure intervals. This impedes study of persistent infections in vulnerable groups, where such information has a limited use. Finally, most methods implicitly assume that transmission events are independent, while common source outbreaks violate this assumption. We propose a maximum likelihood framework SOPHIE (SOcial and PHilogenetic Investigation of Epidemics) based on integration of phylogenetic and random graph models. It infers transmission networks from viral phylogenies and expected properties of inter-host social networks modelled as random graphs with given expected degree distributions. SOPHIE is scalable, accounts for intra-host diversity and accurately infers transmissions without case-specific epidemiological data.

Understanding viral disease progression is vital to the detection of outbreaks and subsequent pla... more Understanding viral disease progression is vital to the detection of outbreaks and subsequent planning for public health actions. Bioinformatics methods are extremely useful for this purpose through a range of applications among which the analysis of viral next-generation sequencing (NGS) data, tracing virus evolution and reconstruction of transmission networks have been explored in this research. The first part of this research focuses on the processing of NGS data where quantification methods are proposed to describe the robustness and reproducibility of the output of bioinformatics tools. This research shows the importance of assessing the reliability of genomic tools. The second part of this study is the application of processed NGS data to investigate the intra-host evolution of Hepatitis C Virus (HCV) to diagnose and detect new and incident HCV cases. A computational method based on Machine Learning algorithms is proposed to solve this problem. This genomic multi feature-based...

Identification of Hepatitis C virus (HCV) infections is crucial in determining viral outbreaks. H... more Identification of Hepatitis C virus (HCV) infections is crucial in determining viral outbreaks. HCV has an affinity to lead towards chronic infection with time due to its highly mutable nature. This leads to increase in heterogeneous population of genetically related HCV variants in the affected individuals. To our knowledge, there are no reliable diagnostic assays for distinguishing acute and chronic HCV infections. Providing a robust classification scheme for the staging of viral infection requires identification of prominent features which in this case can be done using domain knowledge. Simple genetic heterogeneity metrics are not sufficient to represent HCV infections accurately as features for the classification algorithms. This is due to complexity of structural development of intra-host populations, which are affected by bouts of selective sweeps and negative selection during chronic infection [1], [2]. Although some machine learning models are known to work well for sequenc...

Bioinformatics Research and Applications, 2017

Intra-host genetic diversity of hepatitis C virus (HCV) plays crucial role in disease progression... more Intra-host genetic diversity of hepatitis C virus (HCV) plays crucial role in disease progression and treatment outcome. Development of new treatment strategies, generation and validation of new biomedical hypothesis, development of algorithms and models for analysis of viral data and understanding of viral evolution require studying of thousands of intra-host viral populations. Since such amounts of experimental data are not readily available, simulated data are required. However, to the best of our knowledge, currently, there is no a general framework for generation of realistic intra-host HCV populations, which takes into account complex interactions between virus and host, impact of dynamic selection pressures and statistical effects, such as bottleneck and genetic drift.